class_docstr: Construct an MBART tokenizer.
  
  Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
  [SentencePiece](https://github.com/google/sentencepiece).
  
  The tokenization method is `<tokens> <eos> <language code>` for source language documents, and `<language code>
  <tokens> <eos>` for target language documents.
  
  Examples:
  
  ```python
  >>> from transformers import MBartTokenizer
  
  >>> tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-en-ro", src_lang="en_XX", tgt_lang="ro_RO")
  >>> example_english_phrase = " UN Chief Says There Is No Military Solution in Syria"
  >>> expected_translation_romanian = "Şeful ONU declară că nu există o soluţie militară în Siria"
  >>> inputs = tokenizer(example_english_phrase, text_target=expected_translation_romanian, return_tensors="pt")
  ```
docstr: Converts an index (integer) in a token (str) using the vocab.
docstr_tok: ["Converts","an","index","(","integer",")","in","a","token","(","str",")","using","the","vocab","."]
code: def _convert_id_to_token(self, index):
      """Converts an index (integer) in a token (str) using the vocab."""
      if index in self.fairseq_ids_to_tokens:
          return self.fairseq_ids_to_tokens[index]
      return self.sp_model.IdToPiece(index - self.fairseq_offset)
  
imports: import os
  from shutil import copyfile
  from typing import Any, Dict, List, Optional, Tuple
  import sentencepiece
  from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
  from ...utils import logging
label_desc: Use image node_id 14 for calling the MBartTokenizer obj's underlying member method code with example usage: obj._convert_id_to_token(index) and returns: self, self